Transformer-based LLMs in Cybersecurity: An in-depth Study on Log Anomaly Detection and Conversational Defense Mechanisms
Balasubramanian, Prasasthy; Seby, Justin; Kostakos, Panos (2024-01-22)
IEEE
22.01.2024
P. Balasubramanian, J. Seby and P. Kostakos, "Transformer-based LLMs in Cybersecurity: An in-depth Study on Log Anomaly Detection and Conversational Defense Mechanisms," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 3590-3599, doi: 10.1109/BigData59044.2023.10386976
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403202351
https://urn.fi/URN:NBN:fi:oulu-202403202351
Tiivistelmä
Abstract
With the advancement of conversational AI and Large Language Models (LLMs), interactive chatbots are emerging as pivotal assets for connecting with users across various sectors, enabling various capabilities and functions. However, their potential in the cybersecurity domain remains largely untapped. This article introduces a novel method to enhance chatbot performance by incorporating anomaly detection features. Our chatbot uses advanced GPT-3 models and rule-based logic to identify and extract unusual patterns and deviations within logs, making it more proficient in detecting anomalies. We present the architecture and methodology behind our anomaly detection system, showcasing its effectiveness in real-world scenarios. Combining machine learning and domain expertise, our chatbot sets a new standard in interactive, anomaly-aware conversational agents. Our anomaly detection classifier was able to achieve more than 99% of accuracy by illustrating its robust performance in accurately identifying and flagging outliers or unusual patterns in log file data. We also compared the performance of GPT-3 models with other LLMs: BERT, DistilBERT, and ALBERT. Our findings concluded that GPT-3 models consistently outperform all the other LLM models and exhibit significantly higher performance.
With the advancement of conversational AI and Large Language Models (LLMs), interactive chatbots are emerging as pivotal assets for connecting with users across various sectors, enabling various capabilities and functions. However, their potential in the cybersecurity domain remains largely untapped. This article introduces a novel method to enhance chatbot performance by incorporating anomaly detection features. Our chatbot uses advanced GPT-3 models and rule-based logic to identify and extract unusual patterns and deviations within logs, making it more proficient in detecting anomalies. We present the architecture and methodology behind our anomaly detection system, showcasing its effectiveness in real-world scenarios. Combining machine learning and domain expertise, our chatbot sets a new standard in interactive, anomaly-aware conversational agents. Our anomaly detection classifier was able to achieve more than 99% of accuracy by illustrating its robust performance in accurately identifying and flagging outliers or unusual patterns in log file data. We also compared the performance of GPT-3 models with other LLMs: BERT, DistilBERT, and ALBERT. Our findings concluded that GPT-3 models consistently outperform all the other LLM models and exhibit significantly higher performance.
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